Academia lays the foundation for scalable and trustworthy processes as generative AI integrates into additive manufacturing, by focusing on standards, measurement science, open-source research, interdisciplinary education, and hybrid methods. To achieve these goals, academia should:
● Collaborate with industry to develop common practices and data frameworks and support initiatives such as America Makes & American National Standards Institute’s Additive Manufacturing Standardization Collaborative, which has identified 141 standardization gaps across the additive manufacturing lifecycle including design, materials, process, certification and data.
Common industrialized standards streamline processes and quality, reliability, confidence, and enable the government to create comprehensive regulations. With a common set of standards, students will enter the market with skills compatible with multiple companies and provide employers with a ready-to-contribute workforce.
● Prioritize measurement science and metrology through implementing programs such as the National Institute of Standards and Technology’s Measurement Science for Additive Manufacturing program, which seeks to develop in-process sensing, material characterization, process control, and test methods to improve repeatability and part quality across diverse AM processes.
● Aid in developing an open-source research infrastructure and remain neutral with certification and validation datasets. Academia should build shared test beds, neutral fleets of AM machines, open digital-thread platforms, and open generative-design tool chains keeping research unbiased, reproducible, and transparent and accessible to all vendors.
● Sharing reference data, benchmark parts, and validation protocols with industry and government achieves neutrality when providing certifications and validation sets, reducing barriers to AM adoption across sectors.
● Design curricula and training courses that develop interdisciplinary talent focusing on materials science, mechanical engineering, data science, and systems engineering, to produce engineers capable of working at the intersection of AI-driven design, manufacturing, and quality assurance.
● An academic focus on generative AI ethics and sustainability is imperative. As AI integrates into everyday life, consumers’ concerns over its use can be addressed with a comprehensive explanation of its industrial use and how it impacts the environment. Sustainable energy studies can help address the cost and amount of electricity generative AI consumes.
● Emphasize research into hybrid methods by combining machine learning algorithms with physics-based simulation and process-aware constraints, for example, material microstructure, build orientation, and post-processing effects. Improved hybrid methods help ensure optimal designs in theory, while manufacturing with reliable performance.
Academia plays a critical role in the future of generative AI’s use in AM through developing curricula that ensure scalability, reliability, and trust within the industry and consumers. The frameworks created can provide positive collaboration between industry leaders, the workforce, and government that ensure quality, safety, and environmentally friendly.
